Grants
Active Grants
Prediction of Heart Failure Onset using Multimodal Data Analysis, Deep Learning and Commercial Wearables
The aim of this project is to apply machine learning and signal processing techniques to multiple types of data (electronic health records, electrocardiography, and heart rate variability) to predict heart failure onset, and to use wearable devices to improve the predictions and expand their impact to a wider population. The resulting tools will help prevent delayed diagnosis of heart failure, leading to improved medical outcomes, enhanced patient experience, and reduction in healthcare costs.
Project Number: 1 K01 HL155404-01A1
Name of PD/PI: Ansari, Sardar
Source of Support: NIH/NHLBI
Project Dates: 9/1/2021 – 8/31/2026
Total Award Amount: $817,069
A Pilot to Develop and Test ML Models for Detection and Screening of Cardiovascular Conditions within Health Systems
This project aims to build tools that allow for finding patients who suffer from hypertrophic cardiomyopathy. Currently, many patients with this disease are not identified enough for treatment. The study team will use several sources of data that can be used to identify the disease earlier in a federated learning framework in collaboration with AHA and multiple other institutions. This approach protects the patient privacy better than other approaches that are used today. In addition, the study team will explore the implications of using this system in clinics.
Project Number: 24HCMFLP1291285
Name of PD/PI: Ansari, Sardar
Source of Support: American Heart Association
Project Dates: 2/15/2024 – 2/14/2026
Total Award Amount: $498,184
Point-of-care micro-gas chromatography device for diagnosis and monitoring of acute respiratory distress syndrome using exhaled breath signatures
Aim 1: Refine the portable GC device and add VIC detection ability; Aim 2: Using gas chromatography identified breath biomarkers to develop and validate an algorithm based on machine learning and artificial intelligence to detect ARDS and to monitor its trajectory.
Project Number: 1R01HL171517
Name of PD/PI: Fan, Xudong; Sjoding, Michael; Ward, Kevin
Source of Support: NIH
Project Dates: 01/01/2024-12/31/2028
Total Award Amount: $4,169,501
Implementation of an All‐Cause Deterioration Model for Adult Inpatients
The goal of this project is to reduce patients’ risk of experiencing adverse events during their hospital stay by implementing an early warning system called PICTURE (Predicting Inpatient acute Care Transfers and other UnfoReseen Events). We will design and evaluate an efficient, effective, and user-friendly system interface to deliver PICTURE risk scores and their explanations to providers.
Name of PD/PI: Ansari, Sardar
Project Number: AWD019587
Source of Support: The Doctor’s Company Foundation
Project Dates: 11/1/2021-10/31/2024
Total Award Amount: $100,000
Clinical Implementation and Pilot Randomized Controlled Trial of PICTURE-Pediatric (Predicting Intensive Care Transfers and other UnfoReseen Events)
The goal of this project is to prevent inpatient deterioration and adverse events in the pediatric patient population by implementing an early warning system called PICTURE. We will design and evaluate a clinical response that will get triggered when an alert is generated by the model, design an RCT to test the model and the clinical response, and conduct a pilot trial to evaluate the safety and feasibility of the proposed RCT design.
Name of PD/PI: Ansari, Sardar; Daniels, Rodney
Source of Support: The Kahn Pediatric Critical Care Grand Challenge, The Weil Institute for Critical Care Reseach and Innovatoin
Project Dates: 6/1/2024-5/31/2025
Total Award Amount: $99,915
The Application of Artificial Intelligence, Bedside Monitoring, and Patient Reported Outcomes to Improve Patient Safety and Symptom Relief after Paracentesis
Name of PD/PI: Mazumder, Nikhilesh Ray
Source of Support: American College of Gastroenterology
Project Dates: 07/01/2022-06/30/2025
Total Award Amount: $450,000
High performance wearable body odor sensor arrays for disease detection and monitoring
Many diseases, both internal and cutaneous, have distinct odors associated with them, and their identification can provide unique diagnostic clues, guide laboratory evaluation, and facilitate and expedite treatment. Current body odor analysis relies on benchtop instruments, but they are too bulky for use at point-of-care, home or workplace. E-nose technologies provide a simple, light, and low cost alternative for body odor analysis, but they are highly susceptible to environmental changes (e.g., temperature and humidity).
Project Number: 1-U01-TR-004066-01
Name of PD/PI: Fan, Xudong; Gunnerson, Kyle; Huang, Yvonne; Mahajan, Prashant; Gudjonsson, Johann
Source of Support: NIH
Project Dates: 08/10/2022-05/31/2027
Total Award Amount: $5,505,413
Automated Image Processing Systems Capable of Mitigating Uncertainty and Normal anatomical Variations Applied to Acute Respiratory Distress Syndrome (ARDS)
This study aims to develop and test new computational techniques for acute respiratory distress syndrome (ARDS) detection, incorporating two key factors: (1) training models to account for and note uncertainty and (2) leveraging anatomical information.
Name of PD/PI: Negar Farzaneh
Source of Support: Michigan Institute for Clinical & Health Research (MICHR) K12
Project Dates: 05/01/2022 - 09/30/2025
Total Award Amount: $302,962
Past Grants
COVID-19 detection through scent analysis with a compact GC device
The objectives of this project are (1) to refine an automated, portable, high-performance micro-gas chromatography (GC) device and related data analysis / biomarker identification algorithms for rapid (5-6 minutes), in-situ, and sensitive breath analysis and (2) to conduct breath analysis on up to 760 patients and identify and validate the COVID-19 biomarkers in breath.
Project Number: U18TR003812
Name of PD/PI: Fan, Xudong; Ward, Kevin
Source of Support: NIH
Project Dates: 12/21/2020-11/30/2023
Total Award Amount: $1,942,650
EMMETT (Environment for Model Monitoring, Evaluation and Timely Tuning): A Health IT Tool for Clinical Model Surveillance
The main objective of this project is to build a tool for real-time monitoring of clinical decision support systems after deployment into the clinical practice to identify when the performance of the system is declining.
Name of PD/PI: Ansari, Sardar
Source of Support: The Michigan Translational Research and Commercialization for Life Sciences
Project Dates: 04/01/2022-06/30/2023
Total Award Amount: $121,563
Predicting All‐Cause Deterioration for Adult Inpatients using Multi-Modal Data
The goal of this project is to develop a suit of predictive analytics for unexpected patient deterioration on the hospital general wards, as well the tools that enable their deployment.
Name of PD/PI: Ansari, Sardar
Source of Support: Airstrip Technologies, Inc.
Project Dates: 04/01/2022-06/30/2023
Total Award Amount: $1,386,773
Improving Cardiovascular Disease Detection with a Novel Multi-label Classifier for Electrocardiograms: Capturing Label Uncertainty and Complex Hierarchical Relationships between Output Classes
The objective of this project is to develop a multi-label classifier that captures (1) the dependency between different output labels as well as (2) the uncertainty about the ground truth labels in the context of electrocardiogram (ECG) classification.
Name of PD/PI: Negar Farzaneh, Hamid Ghanbari
Source of Support: Michigan Institute for Data Science (MIDAS)
Project Dates: 07/30/2022 - 07/29/2023
Total Award Amount: $29,900
Computer Vision Technologies for Rapid Detection of the Acute Respiratory Distress Syndrome
The aim of this project is to develop computer vision technologies powered by deep convolutional neural networks to automatically identify chest x-ray findings consistent with ARDS with expert level accuracy. This technology will be a fundamental leap forward for ARDS care and will address a critical limitation in the current diagnosis of ARDS.
Name of PD/PI: Sjoding, Michael
Project Dates: 08/01/2020-01/31/2023
Total Award Amount: $545,326
Improved Real-Time Surveillance of Covid-19 Patients’ Electronic Health Records Using Transfer Learning and Ordinal Regression
Name of PD/PI: Admon, Andrew; Gillies, Chris
Source of Support: Michigan Institute for Data Science, University of Michigan
Project Dates: 06/13/2020 – 12/31/2020
Total Award Amount: $30,000
A Novel Non-invasive Piezoelectric Based Sensor for Prediction of Hemodynamic Instability and Assessment of Fluid Responsiveness
Name of PD/PI: Ansari, Sardar
Project Number: F047228
Source of Support: The Michigan Institute for Clinical & Health Research (MICHR), University of Michigan
Project Dates: 01/06/2017 – 05/30/2019
Total Award Amount: $99,722